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Math

 * Analysis of variance
 * Analytic hierarchy process – leader example
 * Ant colony optimization algorithms
 * Artificial neural network
 * Association rule learning
 * Backtracking
 * Backward induction
 * Bayes estimator
 * Bayesian network
 * Bees algorithm
 * Bellman equation
 * Bellman–Ford algorithm
 * Best linear unbiased prediction
 * Bilevel optimization
 * BIRCH (data clustering)
 * Bootstrap aggregating
 * Bootstrapping
 * Boyer–Moore string search algorithm
 * Canadian traveller problem
 * Canonical correlation
 * Cellular automaton
 * Characteristic function (probability theory)
 * Cholesky decomposition
 * Cluster analysis
 * Clustering high-dimensional data
 * Confidence interval
 * Confrontation analysis
 * Consensus clustering
 * Constrained optimization
 * Convex optimization
 * Conway's Game of Life
 * Cooperative game
 * Correlation clustering
 * Correspondence analysis
 * Cramér–Rao bound
 * Critical path method
 * Critical point (mathematics)
 * Cutting stock problem
 * Damerau–Levenshtein distance
 * Decision tree
 * Decision tree learning
 * Default logic
 * Derivative
 * Design of experiments
 * Determinant
 * Dijkstra's algorithm
 * Discrete choice
 * Duality (optimization)
 * Dynamic programming
 * Eigendecomposition of a matrix
 * Eigenvalues and eigenvectors
 * Empirical Bayes method
 * Ensemble learning
 * Errors and residuals in statistics
 * Estimator
 * Expectation–maximization algorithm
 * Extensive-form game
 * Factor analysis
 * Feature learning
 * Finite-state machine
 * Fisher information
 * Fixed effects model
 * Ford–Fulkerson algorithm
 * Game theory
 * Gauss–Markov theorem
 * General linear model
 * Generalized assignment problem
 * Generalized linear model
 * Generalized method of moments
 * Genetic algorithm
 * Genetic programming
 * Gini coefficient
 * Graph coloring
 * Graph theory
 * Greedy algorithm
 * Hessian matrix
 * Hungarian algorithm
 * Identifiability
 * Inductive logic programming
 * Information gain in decision trees
 * Information retrieval
 * Instrumental variable
 * Integer programming
 * Integral
 * Interior point method
 * Jacobian matrix and determinant
 * Jeep problem
 * Job shop scheduling
 * Kalman filter
 * Karush–Kuhn–Tucker conditions
 * Kernel method
 * Kernel regression
 * Knapsack problem
 * Knowledge representation and reasoning
 * Knuth–Morris–Pratt algorithm
 * Kullback–Leibler divergence
 * Lagrange multiplier
 * Lagrangian relaxation
 * Law of cosines
 * Law of cotangents
 * Law of sines
 * Law of tangents
 * Least absolute deviations
 * Least squares
 * Leibniz integral rule
 * Likelihood function
 * Likelihood principle
 * Linear complementarity problem
 * Linear discriminant analysis
 * Linear programming
 * Linear regression
 * Linear-fractional programming
 * Lloyd's algorithm
 * Local regression
 * Logistic regression
 * Low-rank approximation
 * LU decomposition
 * M-estimator
 * Machine translation
 * Markov chain
 * Markov decision process
 * Mathematical optimization
 * Matrix calculus
 * Maximum flow problem
 * Maximum likelihood
 * Mean and predicted response
 * Memetic algorithm
 * Metropolis–Hastings algorithm
 * Minimax
 * Minimum-variance unbiased estimator
 * Mixed logit
 * Mixed model
 * Mixture model
 * Multi-objective optimization
 * Multi-task learning
 * Multicriteria classification
 * Multilevel model
 * Multinomial logistic regression
 * Multiple correspondence analysis
 * Multiple integral
 * Multiple-criteria decision analysis
 * Naive Bayes classifier
 * Nash equilibrium
 * Natural language processing
 * Nearest neighbor search
 * Nelder–Mead method
 * Newsvendor model
 * Newton's method
 * No free lunch in search and optimization
 * Non-linear least squares
 * Non-negative least squares
 * Nonlinear programming
 * Nonlinear regression
 * Nonparametric regression
 * Normal-form game
 * NP-complete
 * Observed information
 * Odds algorithm
 * Optimal control
 * Optimal design
 * Optimal stopping
 * Ordered logit
 * Ordinal optimization
 * Ordinary differential equation
 * Ordinary least squares
 * Orthogonality principle
 * P versus NP problem
 * Parallel metaheuristic
 * Pareto efficiency
 * Parsing
 * Partial correlation
 * Partial derivative
 * Partial differential equation
 * Partial least squares regression
 * Particle swarm optimization
 * Pattern recognition
 * Poisson regression
 * Principal component analysis
 * Principal component regression
 * Probit model
 * Program evaluation and review technique (PERT)
 * Proofs of trigonometric identities
 * Pythagorean theorem
 * QR decomposition
 * Quadratic programming
 * Quantile regression
 * Random effects model
 * Random forest
 * Rank factorization
 * Rao–Blackwell theorem
 * Recursive Bayesian estimation
 * Regression analysis
 * Regression model validation
 * Reinforcement learning
 * Ridge detection
 * Robust optimization
 * Robust regression
 * Score (statistics)
 * Second partial derivative test
 * Seemingly unrelated regressions
 * Self-organizing map
 * Semi-supervised learning
 * Semidefinite programming
 * Semiparametric regression
 * Sensitivity and specificity
 * Shape optimization
 * Similarity learning
 * Simplex algorithm
 * Simpson's paradox
 * Simulated annealing
 * Simultaneous game
 * Singular value decomposition
 * Smoothing spline
 * Sorting algorithm
 * Stable marriage problem
 * Stein's unbiased risk estimate
 * Stochastic process
 * Stochastic programming
 * Stress majorization
 * Structural equation modeling
 * Subgradient method
 * Sufficient statistic
 * Supervised learning
 * Support vector machine
 * Surface integral
 * Swarm intelligence
 * Tabu search
 * Tikhonov regularization
 * Total derivative
 * Transduction (machine learning)
 * Travelling salesman problem
 * Trend estimation
 * Trigonometry
 * Triple product rule
 * Unsupervised learning
 * Volume element
 * Voronoi diagram